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Build ORPO preference pairs for selector v8.
Each preference record: same prompt, chosen='YES' (correct candidate), rejected='NO' (wrong candidate).
Within each BIRD-train question, pair up (YES candidate, NO candidate) records from v8 SFT data
that share the SAME question + db.
Reads: data/sft_selector_v8_pointwise_enriched/train
Writes: data/sft_selector_v8_orpo/{train,test}
For ORPO trainer expected format:
{"prompt": str, "chosen": "YES", "rejected": "NO", ...metadata}
Per Q, with N YES and M NO records, we can make N*M pairs. Cap to max_pairs_per_q for balance.
"""
import argparse, os, sys, random
from collections import defaultdict
os.environ.setdefault("PYTHONNOUSERSITE", "1")
ROOT = "/weka/s225250685/mats-tist"
sys.path.insert(0, ROOT)
from datasets import load_from_disk, Dataset, DatasetDict
def main():
ap = argparse.ArgumentParser()
ap.add_argument("--sft", default=os.path.join(ROOT, "data/sft_selector_v8_pointwise_enriched"))
ap.add_argument("--out", default=os.path.join(ROOT, "data/sft_selector_v8_orpo"))
ap.add_argument("--max_pairs_per_q", type=int, default=4)
args = ap.parse_args()
rng = random.Random(42)
dd = load_from_disk(args.sft)
def make_pairs(rows):
# Group by (question, db_id)
groups = defaultdict(lambda: {"yes": [], "no": []})
for r in rows:
k = (r["question"], r["db_id"])
(groups[k]["yes" if r["is_yes"] else "no"]).append(r)
out = []
for k, g in groups.items():
if not g["yes"] or not g["no"]:
continue
rng.shuffle(g["yes"]); rng.shuffle(g["no"])
pairs_emitted = 0
for y in g["yes"]:
for n in g["no"]:
if pairs_emitted >= args.max_pairs_per_q: break
out.append({
"prompt": y["prompt"],
"chosen": "YES",
"rejected": "NO",
"messages": [
{"role": "user", "content": y["prompt"]},
{"role": "assistant", "content": "YES"},
],
"rejected_messages": [
{"role": "user", "content": n["prompt"]},
{"role": "assistant", "content": "NO"},
],
"question": y["question"],
"db_id": y["db_id"],
})
pairs_emitted += 1
if pairs_emitted >= args.max_pairs_per_q: break
return out
train_pairs = make_pairs(list(dd["train"]))
test_pairs = make_pairs(list(dd["test"]))
rng.shuffle(train_pairs); rng.shuffle(test_pairs)
print(f"train pairs: {len(train_pairs)} test pairs: {len(test_pairs)}")
DatasetDict({
"train": Dataset.from_list(train_pairs),
"test": Dataset.from_list(test_pairs),
}).save_to_disk(args.out)
print(f"SAVED: {args.out}")
if __name__ == "__main__":
main()
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